Drug Use Prevention Playbook: Who’s at Risk, How They Use, and What Protects Them

Peak use at 18–24 • 98% use 3+ substances • 10.4 pp lower heavy use at very high conscientiousness

MakeoverMonday

Data Visualization

R Programming

2025

Young adults (18-24) show peak polydrug use, but conscientiousness offers a protective shield—reducing heavy drug use by 22% among high-risk individuals. A data-driven exploration using the UCI Drug Consumption dataset.

Published

October 21, 2025

Original

The original visualization comes from Drug Consumptions (UCI)

Makeover

Figure 1: Data visualization with three panels analyzing the UCI Drug Consumption dataset (N=1,884). Panel 1: Line chart showing cannabis usage peaks at 80% for ages 18-24, declining with age. Panel 2: Stacked bar charts revealing polydrug patterns, with 18-24 males showing the highest 4+ drug use. Panel 3: Bar chart demonstrating conscientiousness as a protective factor, with a 10.4 percentage point reduction in heavy use between very low and very high conscientiousness groups among high-risk individuals.

Steps to Create this Graphic

1. Load Packages & Setup

Show code

```{r}
#| label: load
#| warning: false
#| message: false
#| results: "hide"

## 1. LOAD PACKAGES & SETUP ----
suppressPackageStartupMessages({
  if (!require("pacman")) install.packages("pacman")
  pacman::p_load(
    tidyverse,  # Easily Install and Load the 'Tidyverse'
    ggtext,     # Improved Text Rendering Support for 'ggplot2'
    showtext,   # Using Fonts More Easily in R Graphs
    scales,     # Scale Functions for Visualization
    glue,       # Interpreted String Literals
    patchwork   # Using Fonts More Easily in R Graphs
  )
})

### |- figure size ----
camcorder::gg_record(
    dir    = here::here("temp_plots"),
    device = "png",
    width  = 14,
    height = 12,
    units  = "in",
    dpi    = 320
)

# Source utility functions
suppressMessages(source(here::here("R/utils/fonts.R")))
source(here::here("R/utils/social_icons.R"))
source(here::here("R/utils/image_utils.R"))
source(here::here("R/themes/base_theme.R"))
```

2. Read in the Data

Show code

```{r}
#| label: read
#| include: true
#| eval: true
#| warning: false
#|
drug_consumption_raw <- read_csv(
  here::here('data/MakeoverMonday/2025/Drug_Consumption.csv')) |> 
  janitor::clean_names()
```

3. Examine the Data

Show code

```{r}
#| label: examine
#| include: true
#| eval: true
#| results: 'hide'
#| warning: false

glimpse(drug_consumption_raw)
skimr::skim_without_charts(drug_consumption_raw)
```

4. Tidy Data

Show code

```{r}
#| label: tidy
#| warning: false

drug_consumption <- drug_consumption_raw |>
  mutate(
    across(alcohol:vsa, ~ case_when(
      .x == "CL0" ~ 0, .x == "CL1" ~ 1, .x == "CL2" ~ 2,
      .x == "CL3" ~ 3, .x == "CL4" ~ 4, .x == "CL5" ~ 5,
      .x == "CL6" ~ 6, TRUE ~ NA_real_
    ))
  )

### |- P1: Age trajectory data ----
age_trajectory_data <- drug_consumption |>
  select(age, cannabis, alcohol, nicotine, coke, ecstasy, lsd) |>
  mutate(age_numeric = case_when(
    age == "18-24" ~ 21, age == "25-34" ~ 29.5, age == "35-44" ~ 39.5,
    age == "45-54" ~ 49.5, age == "55-64" ~ 59.5, age == "65+" ~ 70
  )) |>
  pivot_longer(
    cannabis:lsd,
    names_to = "substance",
    values_to = "usage"
  ) |>
  filter(!is.na(age_numeric)) |>
  group_by(age_numeric, age, substance) |>
  summarise(
    pct_recent = mean(usage >= 3, na.rm = TRUE) * 100,
    .groups = "drop"
  ) |>
  mutate(
    substance = factor(substance,
      levels = c("cannabis", "alcohol", "nicotine", "coke", "ecstasy", "lsd"),
      labels = c("Cannabis", "Alcohol", "Nicotine", "Cocaine", "Ecstasy", "LSD")
    ),
    highlight = substance == "Cannabis"
  )

### |- P2: Polydrugs patterns data ----
polydrug_data <- drug_consumption |>
  select(id, age, gender, alcohol:vsa) |>
  filter(age != "65+") |>
  pivot_longer(
    alcohol:vsa,
    names_to = "drug",
    values_to = "level"
  ) |>
  filter(drug != "semer") |>
  mutate(recent_use = if_else(level >= 3, 1, 0)) |>
  group_by(id, age, gender) |>
  summarise(
    num_drugs = sum(recent_use, na.rm = TRUE),
    .groups = "drop"
  ) |>
  mutate(
    polydrug_category = case_when(
      num_drugs == 0 ~ "None",
      num_drugs == 1 ~ "Single Drug",
      num_drugs == 2 ~ "Two Drugs",
      num_drugs == 3 ~ "Three Drugs",
      num_drugs >= 4 ~ "4+ Drugs"
    ),
    polydrug_category = factor(polydrug_category,
      levels = c(
        "None", "Single Drug", "Two Drugs",
        "Three Drugs", "4+ Drugs"
      )
    ),
    age = factor(age, levels = c("18-24", "25-34", "35-44", "45-54", "55-64"))
  )

### |- P2a: Headline metrics for polydrug use (3+ substances) ----
polydrug_summary <- polydrug_data |>
  count(age, gender, polydrug_category, name = "n") |>
  group_by(age, gender) |>
  mutate(p = n / sum(n)) |>
  summarise(
    share_3plus = sum(p[polydrug_category %in% c("Three Drugs", "4+ Drugs")]),
    .groups = "drop"
  )

# Overall headline for the youngest group (used in the global subtitle)
share_3plus_18_24_overall <- polydrug_summary |>
  filter(age == "18-24") |>
  summarise(value = mean(share_3plus, na.rm = TRUE)) |>
  pull(value)

# Calculate 3+ drug percentages for annotation
p2_labels <- polydrug_data |>
  count(age, gender, polydrug_category) |>
  group_by(age, gender) |>
  mutate(pct = n / sum(n) * 100) |>
  filter(polydrug_category %in% c("Three Drugs", "4+ Drugs")) |>
  group_by(age, gender) |>
  summarise(
    pct_3plus = sum(pct),
    .groups = "drop"
  )

### |- P3: Conscientiousness data ----
conscientiousness_data <- drug_consumption |>
  mutate(
    high_risk = (nscore > 0) & (impulsive > 0) & (ss > 0),
    cscore_category = case_when(
      cscore < quantile(cscore, 0.25, na.rm = TRUE) ~ "Very Low",
      cscore < quantile(cscore, 0.5, na.rm = TRUE) ~ "Low",
      cscore < quantile(cscore, 0.75, na.rm = TRUE) ~ "High",
      TRUE ~ "Very High"
    ),
    cscore_category = factor(cscore_category,
      levels = c("Very Low", "Low", "High", "Very High")
    )
  ) |>
  rowwise() |>
  mutate(
    heavy_illegal = sum(c(
      cannabis, coke, ecstasy, lsd, mushrooms,
      ketamine, amphet
    ) >= 4, na.rm = TRUE) >= 2
  ) |>
  ungroup() |>
  filter(high_risk) |>
  group_by(cscore_category) |>
  summarise(
    pct_heavy_use = mean(heavy_illegal, na.rm = TRUE) * 100,
    n = n(),
    .groups = "drop"
  )

# Calculate reduction
reduction_pp <- conscientiousness_data$pct_heavy_use[1] -
  conscientiousness_data$pct_heavy_use[4]
```

5. Visualization Parameters

Show code

```{r}
#| label: params
#| include: true
#| warning: false

### |-  plot aesthetics ----
# Get base colors with custom palette
colors <- get_theme_colors(
  palette = list(
    danger        = "#B5532F",     
    primary       = "#1E3A5F",
    neutral_dark  = "#5378A6",   
    neutral_mid   = "#9FB6D0",   
    neutral_light = "#E8E8E8",  
    other_lines   = "#B8B8B8",  
    text_primary  = "#2B2D42",
    text_secondary= "#6C757D"
    )
  )   

### |-  titles and caption ----
title_text <- str_glue("Drug Use Prevention Playbook: Who’s at Risk, How They Use, and What Protects Them")

subtitle_text <- str_glue(
    "Peak use at 18–24 • {scales::percent(share_3plus_18_24_overall, accuracy = 1)} use 3+ substances • ",
    "{sprintf('%.1f', reduction_pp)} pp lower heavy use at very high conscientiousness"
  )

# Create caption
caption_text <- create_mm_caption(
  mm_year = current_year,
  mm_week = current_week,
  source_text = "UCI Drug Consumption Dataset (N = 1,884) | Recent use = past year or more frequently"
)

### |-  fonts ----
setup_fonts()
fonts <- get_font_families()

### |-  plot theme ----

# Start with base theme
base_theme <- create_base_theme(colors)

# Add weekly-specific theme elements
weekly_theme <- extend_weekly_theme(
  base_theme,
  theme(
    # # Text styling
    plot.title = element_text(
      size = rel(1.4), family = fonts$title, face = "bold",
      color = colors$title, lineheight = 1.1, hjust = 0,
      margin = margin(t = 5, b = 10)
    ),
    plot.subtitle = element_text(
      size = rel(0.9), family = fonts$subtitle, face = "italic",
      color = alpha(colors$subtitle, 0.9), lineheight = 1.1,
      margin = margin(t = 0, b = 20)
    ),
    
    # Legend formatting
    legend.position = "plot",
    legend.justification = "top",
    legend.margin = margin(l = 12, b = 5),
    legend.key.size = unit(0.8, "cm"),
    legend.box.margin = margin(b = 10),
    legend.title = element_text(face = "bold"),
    
    # Axis formatting
    axis.ticks.y = element_blank(),
    axis.ticks.x = element_line(color = "gray", linewidth = 0.5),
    axis.title.x = element_text(
      face = "bold", size = rel(0.85),
      margin = margin(t = 10)
    ),
    axis.title.y = element_text(
      face = "bold", size = rel(0.85),
      margin = margin(r = 10)
    ),
    axis.text.x = element_text(
      size = rel(0.85), family = fonts$subtitle,
      color = colors$text
    ),
    axis.text.y = element_text(
      size = rel(0.85), family = fonts$subtitle,
      color = colors$text
    ),
    
    # Grid lines
    panel.grid.minor = element_line(color = "#ecf0f1", linewidth = 0.2),
    panel.grid.major = element_line(color = "#ecf0f1", linewidth = 0.4),
    
    # Margin
    plot.margin = margin(20, 20, 20, 20)
  )
)

# Set theme
theme_set(weekly_theme)
```

6. Plot

Show code

```{r}
#| label: plot
#| warning: false

### |- P1: Age trajectory chart ----
p1 <-
ggplot() +
  # Annotate
  annotate("rect", xmin=18, xmax=26, ymin=0, ymax=100,
           fill="#EDEDED", color=NA, alpha=0.35) +
  annotate("text",
    x = 22, y = 95, label = "Peak Risk\nAge Group",
    color = colors$text_secondary, size = 3, fontface = "italic", lineheight = 0.9
  ) +
  annotate("text",
           x = 23, y = 82, label = "Cannabis",
           color = colors$palette$danger, fontface = "bold", size = 4, hjust = 0
  ) +
  annotate("text",
           x = 23, y = 15, label = "Other\nsubstances",
           color = colors$palette$other_lines, size = 3.2, hjust = 0, lineheight = 0.9
  ) +
  # Geoms
  geom_line(
    data = filter(age_trajectory_data, !highlight),
    aes(age_numeric, pct_recent, group = substance),
    color = colors$palette$other_lines, linewidth = 1.2, alpha = 0.7
  ) +
  geom_line(
    data = filter(age_trajectory_data, highlight),
    aes(age_numeric, pct_recent, group = substance),
    color = colors$palette$danger, linewidth = 2.8
  ) +
  geom_point(
    data = filter(age_trajectory_data, highlight),
    aes(age_numeric, pct_recent),
    size = 3.2, color = colors$palette$danger
  ) +
  # Scales
  scale_x_continuous(
    breaks = c(21, 29.5, 39.5, 49.5, 59.5, 70),
    labels = c("18-24", "25-34", "35-44", "45-54", "55-64", "65+")
  ) +
  scale_y_continuous(
    labels = function(x) paste0(x, "%"),
    limits = c(0, 100), expand = c(0, 0)
  ) +
  # Labs
  labs(
    title = "Drug Use Peaks in Young Adulthood",
    subtitle = "Cannabis shows the highest usage rates, especially among 18-24 year olds",
    x = "Age Group",
    y = "Recent Usage Rate (%)"
  ) +
  # Theme
  theme(
    plot.margin = margin(0, 0, 0, 0)
  )

### |- P2: Polydrugs patterns chart ----
p2 <-
ggplot(polydrug_data, aes(age, fill = polydrug_category)) +
  # Geoms
  geom_bar(position = "fill", width = 0.75) +
  # Scales
  scale_y_continuous(labels = percent_format(), expand = c(0, 0.02)) +
  scale_fill_manual(values = c(
    "None"        = "gray",
    "Single Drug" = "gray",
    "Two Drugs"   = colors$palette$neutral_mid,
    "Three Drugs" = colors$palette$neutral_dark,
    "4+ Drugs"    = colors$palette$danger
  )) +
  coord_cartesian(clip = "off", ylim = c(0, 1)) +
  # labs
  labs(
    title = "Young Adults Stack Multiple Substances",
    subtitle = "The 'danger zone' (4+ drugs) is most prevalent among 18-24 year olds",
    x = "Age Group",
    y = "Percentage of Respondents (%)",
    fill = "Number of\nSubstances"
  ) +
  # Facets
  facet_wrap(~gender, labeller = labeller(gender = c(F = "Female", M = "Male"))) +
  # Theme
  theme(
    legend.position = "bottom",
    legend.justification = "top",
    legend.key.size = unit(0.5, "cm"),
    strip.text = element_text(
      face = "bold", 
      color = "gray20", 
      size = rel(1),
      margin = margin(t = 8, b = 8)
    ),
    panel.spacing = unit(2, "lines"),
    plot.margin = margin(0, 0, 0, 0)
  ) +
  guides(fill = guide_legend(nrow = 2, byrow = TRUE))

### |- P3: Conscientiousness chart ----
p3 <-
ggplot(
  conscientiousness_data,
  aes(cscore_category, pct_heavy_use)
) +
  # Geoms
  geom_col(aes(fill = cscore_category), width = 0.65, alpha = 0.95) +
  geom_text(aes(label = sprintf("%.1f%%", pct_heavy_use)),
    vjust = -0.8, fontface = "bold", size = 5.2,
    color = colors$palette$text_primary
  ) +
  # Annotate
  annotate("rect",
    xmin = 0.5, xmax = 2.5, ymin = 0, ymax = Inf,
    fill = alpha(colors$palette$danger, 0.08), color = NA
  ) +
  annotate("segment",
    x = 1.2, xend = 3.8,
    y = max(conscientiousness_data$pct_heavy_use) + 4,
    yend = max(conscientiousness_data$pct_heavy_use) + 4,
    arrow = arrow(ends = "both", length = unit(0.15, "inches")),
    color = colors$palette$text_secondary, linewidth = 0.3
  ) +
  annotate("text",
    x = 2.5,
    y = max(conscientiousness_data$pct_heavy_use) + 5.5,
    label = sprintf("%.1f pp reduction", reduction_pp),
    fontface = "bold", size = 4.2, color = colors$palette$text_primary
  ) +
  annotate("text",
    x = 1.5, y = 2, label = "Risk Zone",
    color = colors$palette$danger, fontface = "bold.italic", size = 3
  ) +
  annotate("text",
    x = 3.5, y = 2, label = "Protected Zone",
    color = colors$palette$neutral_dark, fontface = "bold.italic", size = 3
  ) +
  # Scales
  scale_fill_manual(values = c(
    "Very Low" = colors$palette$danger,
    "Low" = colors$palette$danger,
    "High" = colors$palette$neutral_dark,
    "Very High" = colors$palette$neutral_dark
  )) +
  scale_y_continuous(
    labels = function(x) paste0(x, "%"),
    limits = c(0, max(conscientiousness_data$pct_heavy_use) + 8),
    expand = c(0, 0)
  ) +
  # Labs
  labs(
    title = "Conscientiousness: The Protective Shield",
    subtitle = "High conscientiousness creates a protective effect against heavy drug use in high-risk individuals",
    x = "Conscientiousness Level",
    y = "Heavy Drug Use Rate (%)",
    caption = "High-risk = above avg in neuroticism, impulsivity & sensation seeking | Heavy use = monthly+ use of 2+ illegal drugs"
  ) +
  # Theme
  theme(
    panel.grid.major.x = element_blank(),
    plot.caption = element_text(lineheight = 1.3, size = 8, family = fonts$caption, color = colors$caption),
    plot.margin = margin(0, 0, 0, 0)
  )

### |- Combined Plots ----
combined_plots <- (p1 | p2) / p3 +
  plot_layout(heights = c(1, 2))

combined_plots <- combined_plots +
  plot_annotation(
    title = title_text,
    subtitle = subtitle_text,
    caption = caption_text, 
    theme = theme(
      plot.title = element_text(
        size = rel(1.6),
        family = fonts$title,
        face = "bold",
        color = colors$title,
        lineheight = 1.1,
        margin = margin(t = 5, b = 5)
      ),  
      plot.subtitle = element_markdown(
        size = rel(0.95),
        family = fonts$subtitle,
        color = alpha(colors$subtitle, 0.9),
        lineheight = 1.2,
        margin = margin(t = 5, b = 10)
      ),  
      plot.caption = element_markdown(
        size = rel(0.65),
        family = fonts$caption,
        color = colors$caption,
        hjust = 0.5,
        margin = margin(t = 10)
      )
    )
  )
```

7. Save

Show code

```{r}
#| label: save
#| warning: false

### |-  plot image ----  
save_plot_patchwork(
  plot = combined_plots, 
  type = "makeovermonday", 
  year = current_year,
  week = current_week,
  width = 14, 
  height = 12
  )
```

8. Session Info

Expand for Session Info

R version 4.4.1 (2024-06-14 ucrt)
Platform: x86_64-w64-mingw32/x64
Running under: Windows 11 x64 (build 26100)

Matrix products: default


locale:
[1] LC_COLLATE=English_United States.utf8 
[2] LC_CTYPE=English_United States.utf8   
[3] LC_MONETARY=English_United States.utf8
[4] LC_NUMERIC=C                          
[5] LC_TIME=English_United States.utf8    

time zone: America/New_York
tzcode source: internal

attached base packages:
[1] stats     graphics  grDevices datasets  utils     methods   base     

other attached packages:
 [1] here_1.0.1      patchwork_1.3.0 glue_1.8.0      scales_1.3.0   
 [5] showtext_0.9-7  showtextdb_3.0  sysfonts_0.8.9  ggtext_0.1.2   
 [9] lubridate_1.9.3 forcats_1.0.0   stringr_1.5.1   dplyr_1.1.4    
[13] purrr_1.0.2     readr_2.1.5     tidyr_1.3.1     tibble_3.2.1   
[17] ggplot2_3.5.1   tidyverse_2.0.0 pacman_0.5.1   

loaded via a namespace (and not attached):
 [1] gtable_0.3.6       xfun_0.49          htmlwidgets_1.6.4  tzdb_0.5.0        
 [5] yulab.utils_0.1.8  vctrs_0.6.5        tools_4.4.0        generics_0.1.3    
 [9] curl_6.0.0         parallel_4.4.0     gifski_1.32.0-1    fansi_1.0.6       
[13] pkgconfig_2.0.3    ggplotify_0.1.2    skimr_2.1.5        lifecycle_1.0.4   
[17] compiler_4.4.0     farver_2.1.2       munsell_0.5.1      repr_1.1.7        
[21] janitor_2.2.0      codetools_0.2-20   snakecase_0.11.1   htmltools_0.5.8.1 
[25] yaml_2.3.10        crayon_1.5.3       pillar_1.9.0       camcorder_0.1.0   
[29] magick_2.8.5       commonmark_1.9.2   tidyselect_1.2.1   digest_0.6.37     
[33] stringi_1.8.4      labeling_0.4.3     rsvg_2.6.1         rprojroot_2.0.4   
[37] fastmap_1.2.0      grid_4.4.0         colorspace_2.1-1   cli_3.6.4         
[41] magrittr_2.0.3     base64enc_0.1-3    utf8_1.2.4         withr_3.0.2       
[45] bit64_4.5.2        timechange_0.3.0   rmarkdown_2.29     bit_4.5.0         
[49] hms_1.1.3          evaluate_1.0.1     knitr_1.49         markdown_1.13     
[53] gridGraphics_0.5-1 rlang_1.1.6        gridtext_0.1.5     Rcpp_1.0.13-1     
[57] xml2_1.3.6         renv_1.0.3         svglite_2.1.3      rstudioapi_0.17.1 
[61] vroom_1.6.5        jsonlite_1.8.9     R6_2.5.1           fs_1.6.5          
[65] systemfonts_1.1.0

9. GitHub Repository

Expand for GitHub Repo

The complete code for this analysis is available in mm_2025_41.qmd.

For the full repository, click here.

10. References

Expand for References

Data:

Makeover Monday 2025 Week 41: Drug Consumptions (UCI)

Article

Drug Consumptions (UCI)

Research Paper:

Fehrman, E., Muhammad, A. K., Mirkes, E. M., Egan, V., & Gorban, A. N. (2015). The Five Factor Model of personality and evaluation of drug consumption risk. arXiv preprint. arXiv:1506.06297

11. Custom Functions Documentation

📦 Custom Helper Functions

This analysis uses custom functions from my personal module library for efficiency and consistency across projects.

Functions Used:

fonts.R: setup_fonts(), get_font_families() - Font management with showtext
social_icons.R: create_social_caption() - Generates formatted social media captions
image_utils.R: save_plot() - Consistent plot saving with naming conventions
base_theme.R: create_base_theme(), extend_weekly_theme(), get_theme_colors() - Custom ggplot2 themes

Why custom functions?
These utilities standardize theming, fonts, and output across all my data visualizations. The core analysis (data tidying and visualization logic) uses only standard tidyverse packages.

Source Code:
View all custom functions → GitHub: R/utils

--- title: "Drug Use Prevention Playbook: Who’s at Risk, How They Use, and What Protects Them" subtitle: "Peak use at 18–24 • 98% use 3+ substances • 10.4 pp lower heavy use at very high conscientiousness" description: "Young adults (18-24) show peak polydrug use, but conscientiousness offers a protective shield—reducing heavy drug use by 22% among high-risk individuals. A data-driven exploration using the UCI Drug Consumption dataset." date: "2025-10-21" categories: ["MakeoverMonday", "Data Visualization", "R Programming", "2025"] tags: [ "makeover-monday", "data-visualization", "ggplot2", "public-health", "substance-use", "personality-psychology", "risk-factors", "polydrug-use", "conscientiousness", "young-adults", "tidyverse", "patchwork", "uci-dataset" ] image: "thumbnails/mm_2025_41.png" format: html: toc: true toc-depth: 5 code-link: true code-fold: true code-tools: true code-summary: "Show code" self-contained: true theme: light: [flatly, assets/styling/custom_styles.scss] dark: [darkly, assets/styling/custom_styles_dark.scss] editor_options: chunk_output_type: inline execute: freeze: true cache: true error: false message: false warning: false eval: true --- ```{r} #| label: setup-links #| include: false # CENTRALIZED LINK MANAGEMENT ## Project-specific info current_year <- 2025 current_week <- 41 project_file <- "mm_2025_41.qmd" project_image <- "mm_2025_41.png" ## Data Sources data_main <- "https://data.world/makeovermonday/2025-week-41-drug-consumptions-uci0" data_secondary <- "https://archive.ics.uci.edu/dataset/373/drug+consumption+quantified" ## Repository Links repo_main <- "https://github.com/poncest/personal-website/" repo_file <- paste0("https://github.com/poncest/personal-website/blob/master/data_visualizations/MakeoverMonday/", current_year, "/", project_file) ## External Resources/Images chart_original <- "https://raw.githubusercontent.com/poncest/MakeoverMonday/refs/heads/master/2025/Week_41/original_chart.png" ## Organization/Platform Links org_primary <- "https://www.kaggle.com/datasets/obeykhadija/drug-consumptions-uci?resource=download" org_secondary <- "https://archive.ics.uci.edu/dataset/373/drug+consumption+quantified" # Helper function to create markdown links create_link <- function(text, url) { paste0("[", text, "](", url, ")") } # Helper function for citation-style links create_citation_link <- function(text, url, title = NULL) { if (is.null(title)) { paste0("[", text, "](", url, ")") } else { paste0("[", text, "](", url, ' "', title, '")') } } ``` ### Original The original visualization comes from `r create_link("Drug Consumptions (UCI)", data_secondary)`  ![Original visualization](https://raw.githubusercontent.com/poncest/MakeoverMonday/refs/heads/master/2025/Week_41/original_chart.png) ### Makeover ![Data visualization with three panels analyzing the UCI Drug Consumption dataset (N=1,884). Panel 1: Line chart showing cannabis usage peaks at 80% for ages 18-24, declining with age. Panel 2: Stacked bar charts revealing polydrug patterns, with 18-24 males showing the highest 4+ drug use. Panel 3: Bar chart demonstrating conscientiousness as a protective factor, with a 10.4 percentage point reduction in heavy use between very low and very high conscientiousness groups among high-risk individuals.](mm_2025_41.png){#fig-1} ### <mark> **Steps to Create this Graphic** </mark> #### 1. Load Packages & Setup ```{r} #| label: load #| warning: false #| message: false #| results: "hide" ## 1. LOAD PACKAGES & SETUP ---- suppressPackageStartupMessages({ if (!require("pacman")) install.packages("pacman") pacman::p_load( tidyverse, # Easily Install and Load the 'Tidyverse' ggtext, # Improved Text Rendering Support for 'ggplot2' showtext, # Using Fonts More Easily in R Graphs scales, # Scale Functions for Visualization glue, # Interpreted String Literals patchwork # Using Fonts More Easily in R Graphs ) }) ### |- figure size ---- camcorder::gg_record( dir = here::here("temp_plots"), device = "png", width = 14, height = 12, units = "in", dpi = 320 ) # Source utility functions suppressMessages(source(here::here("R/utils/fonts.R"))) source(here::here("R/utils/social_icons.R")) source(here::here("R/utils/image_utils.R")) source(here::here("R/themes/base_theme.R")) ``` #### 2. Read in the Data ```{r} #| label: read #| include: true #| eval: true #| warning: false #| drug_consumption_raw <- read_csv( here::here('data/MakeoverMonday/2025/Drug_Consumption.csv')) |> janitor::clean_names() ``` #### 3. Examine the Data ```{r} #| label: examine #| include: true #| eval: true #| results: 'hide' #| warning: false glimpse(drug_consumption_raw) skimr::skim_without_charts(drug_consumption_raw) ``` #### 4. Tidy Data ```{r} #| label: tidy #| warning: false drug_consumption <- drug_consumption_raw |> mutate( across(alcohol:vsa, ~ case_when( .x == "CL0" ~ 0, .x == "CL1" ~ 1, .x == "CL2" ~ 2, .x == "CL3" ~ 3, .x == "CL4" ~ 4, .x == "CL5" ~ 5, .x == "CL6" ~ 6, TRUE ~ NA_real_ )) ) ### |- P1: Age trajectory data ---- age_trajectory_data <- drug_consumption |> select(age, cannabis, alcohol, nicotine, coke, ecstasy, lsd) |> mutate(age_numeric = case_when( age == "18-24" ~ 21, age == "25-34" ~ 29.5, age == "35-44" ~ 39.5, age == "45-54" ~ 49.5, age == "55-64" ~ 59.5, age == "65+" ~ 70 )) |> pivot_longer( cannabis:lsd, names_to = "substance", values_to = "usage" ) |> filter(!is.na(age_numeric)) |> group_by(age_numeric, age, substance) |> summarise( pct_recent = mean(usage >= 3, na.rm = TRUE) * 100, .groups = "drop" ) |> mutate( substance = factor(substance, levels = c("cannabis", "alcohol", "nicotine", "coke", "ecstasy", "lsd"), labels = c("Cannabis", "Alcohol", "Nicotine", "Cocaine", "Ecstasy", "LSD") ), highlight = substance == "Cannabis" ) ### |- P2: Polydrugs patterns data ---- polydrug_data <- drug_consumption |> select(id, age, gender, alcohol:vsa) |> filter(age != "65+") |> pivot_longer( alcohol:vsa, names_to = "drug", values_to = "level" ) |> filter(drug != "semer") |> mutate(recent_use = if_else(level >= 3, 1, 0)) |> group_by(id, age, gender) |> summarise( num_drugs = sum(recent_use, na.rm = TRUE), .groups = "drop" ) |> mutate( polydrug_category = case_when( num_drugs == 0 ~ "None", num_drugs == 1 ~ "Single Drug", num_drugs == 2 ~ "Two Drugs", num_drugs == 3 ~ "Three Drugs", num_drugs >= 4 ~ "4+ Drugs" ), polydrug_category = factor(polydrug_category, levels = c( "None", "Single Drug", "Two Drugs", "Three Drugs", "4+ Drugs" ) ), age = factor(age, levels = c("18-24", "25-34", "35-44", "45-54", "55-64")) ) ### |- P2a: Headline metrics for polydrug use (3+ substances) ---- polydrug_summary <- polydrug_data |> count(age, gender, polydrug_category, name = "n") |> group_by(age, gender) |> mutate(p = n / sum(n)) |> summarise( share_3plus = sum(p[polydrug_category %in% c("Three Drugs", "4+ Drugs")]), .groups = "drop" ) # Overall headline for the youngest group (used in the global subtitle) share_3plus_18_24_overall <- polydrug_summary |> filter(age == "18-24") |> summarise(value = mean(share_3plus, na.rm = TRUE)) |> pull(value) # Calculate 3+ drug percentages for annotation p2_labels <- polydrug_data |> count(age, gender, polydrug_category) |> group_by(age, gender) |> mutate(pct = n / sum(n) * 100) |> filter(polydrug_category %in% c("Three Drugs", "4+ Drugs")) |> group_by(age, gender) |> summarise( pct_3plus = sum(pct), .groups = "drop" ) ### |- P3: Conscientiousness data ---- conscientiousness_data <- drug_consumption |> mutate( high_risk = (nscore > 0) & (impulsive > 0) & (ss > 0), cscore_category = case_when( cscore < quantile(cscore, 0.25, na.rm = TRUE) ~ "Very Low", cscore < quantile(cscore, 0.5, na.rm = TRUE) ~ "Low", cscore < quantile(cscore, 0.75, na.rm = TRUE) ~ "High", TRUE ~ "Very High" ), cscore_category = factor(cscore_category, levels = c("Very Low", "Low", "High", "Very High") ) ) |> rowwise() |> mutate( heavy_illegal = sum(c( cannabis, coke, ecstasy, lsd, mushrooms, ketamine, amphet ) >= 4, na.rm = TRUE) >= 2 ) |> ungroup() |> filter(high_risk) |> group_by(cscore_category) |> summarise( pct_heavy_use = mean(heavy_illegal, na.rm = TRUE) * 100, n = n(), .groups = "drop" ) # Calculate reduction reduction_pp <- conscientiousness_data$pct_heavy_use[1] - conscientiousness_data$pct_heavy_use[4] ``` #### 5. Visualization Parameters ```{r} #| label: params #| include: true #| warning: false ### |- plot aesthetics ---- # Get base colors with custom palette colors <- get_theme_colors( palette = list( danger = "#B5532F", primary = "#1E3A5F", neutral_dark = "#5378A6", neutral_mid = "#9FB6D0", neutral_light = "#E8E8E8", other_lines = "#B8B8B8", text_primary = "#2B2D42", text_secondary= "#6C757D" ) ) ### |- titles and caption ---- title_text <- str_glue("Drug Use Prevention Playbook: Who’s at Risk, How They Use, and What Protects Them") subtitle_text <- str_glue( "Peak use at 18–24 • {scales::percent(share_3plus_18_24_overall, accuracy = 1)} use 3+ substances • ", "{sprintf('%.1f', reduction_pp)} pp lower heavy use at very high conscientiousness" ) # Create caption caption_text <- create_mm_caption( mm_year = current_year, mm_week = current_week, source_text = "UCI Drug Consumption Dataset (N = 1,884) | Recent use = past year or more frequently" ) ### |- fonts ---- setup_fonts() fonts <- get_font_families() ### |- plot theme ---- # Start with base theme base_theme <- create_base_theme(colors) # Add weekly-specific theme elements weekly_theme <- extend_weekly_theme( base_theme, theme( # # Text styling plot.title = element_text( size = rel(1.4), family = fonts$title, face = "bold", color = colors$title, lineheight = 1.1, hjust = 0, margin = margin(t = 5, b = 10) ), plot.subtitle = element_text( size = rel(0.9), family = fonts$subtitle, face = "italic", color = alpha(colors$subtitle, 0.9), lineheight = 1.1, margin = margin(t = 0, b = 20) ), # Legend formatting legend.position = "plot", legend.justification = "top", legend.margin = margin(l = 12, b = 5), legend.key.size = unit(0.8, "cm"), legend.box.margin = margin(b = 10), legend.title = element_text(face = "bold"), # Axis formatting axis.ticks.y = element_blank(), axis.ticks.x = element_line(color = "gray", linewidth = 0.5), axis.title.x = element_text( face = "bold", size = rel(0.85), margin = margin(t = 10) ), axis.title.y = element_text( face = "bold", size = rel(0.85), margin = margin(r = 10) ), axis.text.x = element_text( size = rel(0.85), family = fonts$subtitle, color = colors$text ), axis.text.y = element_text( size = rel(0.85), family = fonts$subtitle, color = colors$text ), # Grid lines panel.grid.minor = element_line(color = "#ecf0f1", linewidth = 0.2), panel.grid.major = element_line(color = "#ecf0f1", linewidth = 0.4), # Margin plot.margin = margin(20, 20, 20, 20) ) ) # Set theme theme_set(weekly_theme) ``` #### 6. Plot ```{r} #| label: plot #| warning: false ### |- P1: Age trajectory chart ---- p1 <- ggplot() + # Annotate annotate("rect", xmin=18, xmax=26, ymin=0, ymax=100, fill="#EDEDED", color=NA, alpha=0.35) + annotate("text", x = 22, y = 95, label = "Peak Risk\nAge Group", color = colors$text_secondary, size = 3, fontface = "italic", lineheight = 0.9 ) + annotate("text", x = 23, y = 82, label = "Cannabis", color = colors$palette$danger, fontface = "bold", size = 4, hjust = 0 ) + annotate("text", x = 23, y = 15, label = "Other\nsubstances", color = colors$palette$other_lines, size = 3.2, hjust = 0, lineheight = 0.9 ) + # Geoms geom_line( data = filter(age_trajectory_data, !highlight), aes(age_numeric, pct_recent, group = substance), color = colors$palette$other_lines, linewidth = 1.2, alpha = 0.7 ) + geom_line( data = filter(age_trajectory_data, highlight), aes(age_numeric, pct_recent, group = substance), color = colors$palette$danger, linewidth = 2.8 ) + geom_point( data = filter(age_trajectory_data, highlight), aes(age_numeric, pct_recent), size = 3.2, color = colors$palette$danger ) + # Scales scale_x_continuous( breaks = c(21, 29.5, 39.5, 49.5, 59.5, 70), labels = c("18-24", "25-34", "35-44", "45-54", "55-64", "65+") ) + scale_y_continuous( labels = function(x) paste0(x, "%"), limits = c(0, 100), expand = c(0, 0) ) + # Labs labs( title = "Drug Use Peaks in Young Adulthood", subtitle = "Cannabis shows the highest usage rates, especially among 18-24 year olds", x = "Age Group", y = "Recent Usage Rate (%)" ) + # Theme theme( plot.margin = margin(0, 0, 0, 0) ) ### |- P2: Polydrugs patterns chart ---- p2 <- ggplot(polydrug_data, aes(age, fill = polydrug_category)) + # Geoms geom_bar(position = "fill", width = 0.75) + # Scales scale_y_continuous(labels = percent_format(), expand = c(0, 0.02)) + scale_fill_manual(values = c( "None" = "gray", "Single Drug" = "gray", "Two Drugs" = colors$palette$neutral_mid, "Three Drugs" = colors$palette$neutral_dark, "4+ Drugs" = colors$palette$danger )) + coord_cartesian(clip = "off", ylim = c(0, 1)) + # labs labs( title = "Young Adults Stack Multiple Substances", subtitle = "The 'danger zone' (4+ drugs) is most prevalent among 18-24 year olds", x = "Age Group", y = "Percentage of Respondents (%)", fill = "Number of\nSubstances" ) + # Facets facet_wrap(~gender, labeller = labeller(gender = c(F = "Female", M = "Male"))) + # Theme theme( legend.position = "bottom", legend.justification = "top", legend.key.size = unit(0.5, "cm"), strip.text = element_text( face = "bold", color = "gray20", size = rel(1), margin = margin(t = 8, b = 8) ), panel.spacing = unit(2, "lines"), plot.margin = margin(0, 0, 0, 0) ) + guides(fill = guide_legend(nrow = 2, byrow = TRUE)) ### |- P3: Conscientiousness chart ---- p3 <- ggplot( conscientiousness_data, aes(cscore_category, pct_heavy_use) ) + # Geoms geom_col(aes(fill = cscore_category), width = 0.65, alpha = 0.95) + geom_text(aes(label = sprintf("%.1f%%", pct_heavy_use)), vjust = -0.8, fontface = "bold", size = 5.2, color = colors$palette$text_primary ) + # Annotate annotate("rect", xmin = 0.5, xmax = 2.5, ymin = 0, ymax = Inf, fill = alpha(colors$palette$danger, 0.08), color = NA ) + annotate("segment", x = 1.2, xend = 3.8, y = max(conscientiousness_data$pct_heavy_use) + 4, yend = max(conscientiousness_data$pct_heavy_use) + 4, arrow = arrow(ends = "both", length = unit(0.15, "inches")), color = colors$palette$text_secondary, linewidth = 0.3 ) + annotate("text", x = 2.5, y = max(conscientiousness_data$pct_heavy_use) + 5.5, label = sprintf("%.1f pp reduction", reduction_pp), fontface = "bold", size = 4.2, color = colors$palette$text_primary ) + annotate("text", x = 1.5, y = 2, label = "Risk Zone", color = colors$palette$danger, fontface = "bold.italic", size = 3 ) + annotate("text", x = 3.5, y = 2, label = "Protected Zone", color = colors$palette$neutral_dark, fontface = "bold.italic", size = 3 ) + # Scales scale_fill_manual(values = c( "Very Low" = colors$palette$danger, "Low" = colors$palette$danger, "High" = colors$palette$neutral_dark, "Very High" = colors$palette$neutral_dark )) + scale_y_continuous( labels = function(x) paste0(x, "%"), limits = c(0, max(conscientiousness_data$pct_heavy_use) + 8), expand = c(0, 0) ) + # Labs labs( title = "Conscientiousness: The Protective Shield", subtitle = "High conscientiousness creates a protective effect against heavy drug use in high-risk individuals", x = "Conscientiousness Level", y = "Heavy Drug Use Rate (%)", caption = "High-risk = above avg in neuroticism, impulsivity & sensation seeking | Heavy use = monthly+ use of 2+ illegal drugs" ) + # Theme theme( panel.grid.major.x = element_blank(), plot.caption = element_text(lineheight = 1.3, size = 8, family = fonts$caption, color = colors$caption), plot.margin = margin(0, 0, 0, 0) ) ### |- Combined Plots ---- combined_plots <- (p1 | p2) / p3 + plot_layout(heights = c(1, 2)) combined_plots <- combined_plots + plot_annotation( title = title_text, subtitle = subtitle_text, caption = caption_text, theme = theme( plot.title = element_text( size = rel(1.6), family = fonts$title, face = "bold", color = colors$title, lineheight = 1.1, margin = margin(t = 5, b = 5) ), plot.subtitle = element_markdown( size = rel(0.95), family = fonts$subtitle, color = alpha(colors$subtitle, 0.9), lineheight = 1.2, margin = margin(t = 5, b = 10) ), plot.caption = element_markdown( size = rel(0.65), family = fonts$caption, color = colors$caption, hjust = 0.5, margin = margin(t = 10) ) ) ) ``` #### 7. Save ```{r} #| label: save #| warning: false ### |- plot image ---- save_plot_patchwork( plot = combined_plots, type = "makeovermonday", year = current_year, week = current_week, width = 14, height = 12 ) ``` #### 8. Session Info ::: {.callout-tip collapse="true"} ##### Expand for Session Info ```{r, echo = FALSE} #| eval: true #| warning: false sessionInfo() ``` ::: #### 9. GitHub Repository ::: {.callout-tip collapse="true"} ##### Expand for GitHub Repo The complete code for this analysis is available in `r create_link(project_file, repo_file)`. For the full repository, `r create_link("click here", repo_main)`. ::: #### 10. References ::: {.callout-tip collapse="true"} ##### Expand for References 1. Data: - Makeover Monday `r current_year` Week `r current_week`: `r create_link("Drug Consumptions (UCI)", data_main)` 2. Article - `r create_link("Drug Consumptions (UCI)", data_secondary)` 3. Research Paper: - Fehrman, E., Muhammad, A. K., Mirkes, E. M., Egan, V., & Gorban, A. N. (2015). The Five Factor Model of personality and evaluation of drug consumption risk. arXiv preprint. `r create_link("arXiv:1506.06297", "https://arxiv.org/abs/1506.06297")` ::: #### 11. Custom Functions Documentation ::: {.callout-note collapse="true"} ##### 📦 Custom Helper Functions This analysis uses custom functions from my personal module library for efficiency and consistency across projects. **Functions Used:** - **`fonts.R`**: `setup_fonts()`, `get_font_families()` - Font management with showtext - **`social_icons.R`**: `create_social_caption()` - Generates formatted social media captions - **`image_utils.R`**: `save_plot()` - Consistent plot saving with naming conventions - **`base_theme.R`**: `create_base_theme()`, `extend_weekly_theme()`, `get_theme_colors()` - Custom ggplot2 themes **Why custom functions?**\ These utilities standardize theming, fonts, and output across all my data visualizations. The core analysis (data tidying and visualization logic) uses only standard tidyverse packages. **Source Code:**\ View all custom functions → [GitHub: R/utils](https://github.com/poncest/personal-website/tree/master/R) :::